The Potential Impact of Vehicle-to-Vehicle Communication on On-Street Parking Under Heterogeneous Conditions
The aim of this paper is to study the impacts of bottom-up information provision about on-street parking places on parking dynamics under heterogeneous conditions. Using an agent-based simulation model, performance is compared between a bottom-up vehicle-to-vehicle communication strategy and a strategy that combines parking sensors and vehicle-to-vehicle communication. In the latter approach on street parking places are all equipped with sensors capable of disseminating their status. The results show that search time is decreased for informed 'smart' cars, especially under spatially heterogeneous conditions, for the sensor-based strategy. Furthermore, for the case of the sensor-based strategy, the results point out that smart cars outperform regular cars in terms of walking distance under all circumstances. The positive impacts for the vehicle-to-vehicle strategy are limited to walking distance improvements only.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/19391390
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Supplemental Notes:
- Abstract reprinted with permission of IEEE.
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Authors:
- Tasseron, Geert
- Martens, Karel
- van der Heijden, Rob
- Publication Date: 2016
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 33-42
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Serial:
- IEEE Intelligent Transportation Systems Magazine
- Volume: 8
- Issue Number: 2
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1939-1390
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5117645
Subject/Index Terms
- TRT Terms: Mathematical models; On street parking; Sensors; Simulation; Vehicle to vehicle communications
- Uncontrolled Terms: Agent based models; Heterogeneity (Traffic)
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;
Filing Info
- Accession Number: 01599667
- Record Type: Publication
- Files: TRIS
- Created Date: May 20 2016 3:54PM